gan-implicit-generative-model

IN premise — entries/2026/06/21/wiki-Generative_adversarial_network-chunk-1.md

Created 2026-06-21T09:55:49+00:00

GANs are implicit generative models that do not explicitly model the likelihood function and cannot find the latent variable for a given sample, unlike flow-based models or VAEs

Dependents

These beliefs depend on this one:

• IN gan-implicit-nature-explains-training-difficulty — GANs' implicit generative nature (no explicit likelihood function) is plausibly connected to their need for multiple complementary training interventions — without a tractable objective to optimize directly, training stability relies on several distinct design choices (non-saturating loss, TTUR, deterministic discriminators) each addressing a different failure mode, which may partly substitute for the more direct optimization signal that explicit-likelihood models enjoy.